1. Statistical Approach to a Color-based Face Detection Algorithm
EE 368
Digital Image Processing
Group 15
Carmen Ng, Thomas Pun
May 30, 2002

2. Statistical Approach to a Color-based Face Detection Algorithm
Assumptions
4 Stages:
Pre-processing
Skin Color Region Labeling
Statistical Face Selection Techniques
Edge Detection
Advantages/Disadvantages
11/21/2018

3. Statistical Approach to a Color-based Face Detection Algorithm
Assumptions:
Color image
Multiple faces with similar area
Face orientation
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4. Statistical Approach to a Color-based Face Detection Algorithm
I . Image Pre-processing
Boundary extension
Improves accuracy
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5. Statistical Approach to a Color-based Face Detection Algorithm
II . Skin Color Region Labeling
Color-based
Chrominance extraction in YCbCr space
Morphological operations
Dilation and erosion
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6. <= Original Image
Rough Mask =>
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7. Binary Mask after Morphological Operations
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8. Statistical Approach to a Color-based Face Detection Algorithm
III . Statistical Analysis
Popular area finder
Facial feature detector (holes in binary images)
Popular area, width and height
Face rejection
Reject unpopular areas
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9. Selected Face Regions after Stage III
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10. Statistical Approach to a Color-based Face Detection Algorithm
IV . Facial Feature (Eye) Detection
Approximate eye location
LPF to remove noise
Edge detection to locate strong edges
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11. After LPF and Edge Detection
Typical Background
Typical Face
After LPF and Edge Detection
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12. Statistical Approach to a Color-based Face Detection Algorithm
Results/Conclusions:
88% success rate
Adv: fast, no training required,work with video compression std.
DisAdv: min of faces required in image, work best with reliable facial detector
11/21/2018